NeurIPS (Neural Information Processing Systems), widely considered the world’s largest AI and machine learning research conference, has published the list of the 1900 accepted papers for its thirty-fourth edition.

More than 30 of them include authors from the DATAIA's ecosystem:

• A Bandit Learning Algorithm and Applications to Auction Design // Kim Thang Nguyen (IBISC, University Paris-Saclay)
• A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons // Gabriel Mahuas (ENS Paris-Saclay; IST Austria; LPENS) · Giulio Isacchini (Max Planck Institute for Dynamics and Selforganisation) · Olivier Marre (Institut de la vision) · Ulisse Ferrari (Universite Pier et Marie Curie) · Thierry Mora (ENS)
• A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
 // Gersende Fort (CNRS) · Eric Moulines (Ecole Polytechnique) · Hoi-To Wai (The Chinese University of Hong Kong)
• Correspondence learning via linearly-invariant embedding // Riccardo Marin (University of Verona) · Marie-Julie Rakotosaona (Ecole Polytechnique) · Simone Melzi (University of Verona) · Maks Ovsjanikov (Ecole polytechnique)
• Deep Statistical Solvers // Balthazar Donon (RTE R&D / Université Paris-Saclay) · Zhengying Liu (Inria/U. Paris-Sud) · Wenzhuo LIU (Inria Paris Saclay) · Isabelle Guyon (U. Paris-Saclay & ChaLearn) · Antoine Marot (RTE) · Marc Schoenauer (INRIA)
• Efficient Topological Layer based on Persistent Landscapes
// Kwangho Kim (Carnegie Mellon University) · Jisu Kim (Inria Saclay) · Manzil Zaheer (Google Research) · Joon Kim (Carnegie Mellon University) · Frederic Chazal (INRIA) · Larry Wasserman (Carnegie Mellon University)
• Entropic Optimal Transport between (Unbalanced) Gaussian Measures has a Closed Form (oral) // Hicham Janati (Inria / ENSAE) · Boris Muzellec (ENSAE, Institut Polytechnique de Paris) · Gabriel Peyré (CNRS and ENS) · Marco Cuturi (Google Brain & CREST - ENSAE)
• Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data
// Aude Sportisse (Sorbonne University, Ecole Polytechnique) · Claire Boyer (LPSM, Sorbonne Université) · Julie Josse (CMAP / CNRS)
• Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits // Arya Akhavan (ENSAE - IIT) · Massimiliano Pontil (IIT & UCL) · Alexandre Tsybakov (CREST, ENSAE)
• Fair regression via plug-in estimator and recalibration with statistical guarantees
// Evgenii Chzhen (Université Paris-Saclay) · Christophe Denis (Universite Paris Est) · Mohamed Hebiri (Université Gustave Eiffel) · Luca Oneto (University of Genoa) · Massimiliano Pontil (IIT)
• Fair regression with Wasserstein barycenters
// Evgenii Chzhen (Université Paris-Saclay) · Christophe Denis (Universite Paris Est) · Mohamed Hebiri (Université Gustave Eiffel) · Luca Oneto (University of Genoa) · Massimiliano Pontil (IIT)
• Finite Contiuum-Armed Bandits // Solenne Gaucher (Université Paris-Saclay)
• Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm // Tianyi Lin (UC Berkeley) · Nhat Ho (University of Texas at Austin) · Xi Chen (New York University) · Marco Cuturi (Google Brain & CREST - ENSAE) · Michael Jordan (UC Berkeley)
• Hard Shape-Constrained Kernel Machines // Pierre-Cyril Aubin-Frankowski (MINES ParisTech) · Zoltan Szabo (Ecole Polytechnique)
• Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
// Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford) · Ozan Sener (Intel Labs) · George Deligiannidis (Oxford) · Murat Erdogdu (University of Toronto)
• Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
// Hamid Jalalzai (Télécom ParisTech) · Pierre Colombo (Telecom ParisTech) · Chloé Clavel (Telecom-ParisTech, Paris, France) · Eric Gaussier (Université Joseph Fourier, Grenoble) · Giovanna Varni (Telecom ParisTec) · Emmanuel Vignon (IBM) · Anne Sabourin (LTCI, Telecom ParisTech, Université Paris-Saclay)
• Information Maximization for Few-Shot Learning
// Malik Boudiaf (Ecole de Technologie Superieure) · Imtiaz Ziko (Ecole de technologie superieure (ETS)) · Jérôme Rony (ÉTS Montréal) · Jose Dolz (ETS Montreal) · Pablo Piantanida (CentraleSupélec - Mila) · Ismail Ben Ayed (ETS Montreal)
• Learning to solve TV regularised problems with unrolled algorithms
// Hamza Cherkaoui (CEA) · Jeremias Sulam (Johns Hopkins University) · Thomas Moreau (Inria)
• Learning with Differentiable Pertubed Optimizers // Quentin Berthet (Google Brain) · Mathieu Blondel (Google) · Olivier Teboul (Ecole Centrale Paris) · Marco Cuturi (Google Brain & CREST - ENSAE) · Jean-Philippe Vert () · Francis Bach (INRIA - Ecole Normale Superieure)
• Linear Time Sinkhorn Divergences using Positive Features // Meyer Scetbon (CREST-ENSAE) · Marco Cuturi (Google Brain & CREST - ENSAE)
• Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
// Thomas Berrett (CREST, ENSAE, Institut Polytechnique de Paris) · Cristina Butucea (CREST, ENSAE, Institut Polytechnique de Paris)
• Modeling Shared responses in Neuroimaging Studies through MultiView ICA
// Hugo Richard (INRIA) · Luigi Gresele (MPI for Intelligent Systems, Tübingen) · Aapo Hyvarinen (University of Helsinki) · Bertrand Thirion (INRIA) · Alexandre Gramfort (INRIA) · Pierre Ablin (INRIA)
• Neumann networks: differential programming for supervised learning with missing values (oral) // Marine Le Morvan (INRIA) · Julie Josse (CMAP / CNRS) · Thomas Moreau (Inria) · Erwan Scornet (Ecole Polytechnique) · Gael Varoquaux (Parietal Team, INRIA)
• Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets // Avetik Karagulyan (Center for Research in Economics and Statistics / ENSAE / IPP) · Arnak Dalalyan (ENSAE ParisTech)
• PEP: Parameter Ensembling by Perturbation
// Alireza Mehrtash (University of British Columbia) · Purang Abolmaesumi (UBC) · Polina Golland (Massachusetts Institute of Technology) · Tina Kapur (Brigham and Women's Hospital) · Demian Wassermann (Inria) · William Wells (Harvard Medical School)
• Projection Robust Wasserstein Distance and Riemannian Optimization // Tianyi Lin (UC Berkeley) · Chenyou Fan (The Chinese University of Hong Kong, Shenzhen) · Nhat Ho (University of Texas at Austin) · Marco Cuturi (Google Brain & CREST - ENSAE) · Michael Jordan (UC Berkeley)
• Quantitative Propagation of Chaos for SGD in Wide Neural Networks
// Valentin De Bortoli (ENS Paris-Saclay) · Alain Durmus (ENS Paris Saclay) · Xavier Fontaine (ENS Paris-Saclay) · Umut Simsekli (Institut Polytechnique de Paris / University of Oxford)
• Quantized Variational Inference
// Amir DIB (Paris-Saclay University, ENS Paris-Saclay)
• Random Walk Graph Neural Networks // Giannis Nikolentzos (Athens University of Economics and Business) · Michalis Vazirgiannis (École Polytechnique)
• Relative gradient optimization of the Jacobian term in unsupervised deep learning
// Luigi Gresele (MPI for Intelligent Systems, Tübingen) · Giancarlo Fissore (Inria) · Adrián Javaloy Bornás (Max Planck Institute for Intelligent Systems) · Bernhard Schölkopf (MPI for Intelligent Systems) · Aapo Hyvarinen (University of Helsinki)
• Robustness of Community Detection to Random Geometric Perturbations // Sandrine Peche (LPSM, Université Paris Diderot) · Vianney Perchet (ENSAE & Criteo AI Lab)
• Smooth And Consistent Probabilistic Regression Trees
// Sami Alkhoury (University Grenoble Alpes) · Emilie Devijver (CNRS - UGA) · Marianne Clausel (IECL) · Myriam Tami (Université Paris-Saclay) · Eric Gaussier (Université Joseph Fourier, Grenoble) · georges Oppenheim (Private)
• Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers // Kiwon Um (Telecom Paris, IP Paris) · Yun (Raymond) Fei (Columbia University) · Philipp Holl (Technical University of Munich) · Robert Brand (Technical University of Munich) · Nils Thuerey (Technical University of Munich)
• Statistical and Topological Properties of Sliced Probability Divergences // Kimia Nadjahi (Télécom ParisTech) · Alain Durmus (ENS Paris Saclay) · Lénaïc Chizat (CNRS) · Soheil Kolouri (HRL Laboratories LLC) · Shahin Shahrampour (Texas A&M University) · Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso
// Jerome-Alexis Chevalier (Inria Saclay Île-de-France) · Joseph Salmon (Université de Montpellier) · Alexandre Gramfort (INRIA) · Bertrand Thirion (INRIA)
• Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits // Pierre Perrault (INRIA - ENS Paris Saclay) · Etienne Boursier (ENS Paris Saclay) · Michal Valko (DeepMind) · Vianney Perchet (ENSAE & Criteo AI Lab)
• Towards Better Generalization of Adaptive Gradient Methods
// Yingxue Zhou (University of Minnesota) · Belhal Karimi (Ecole Polytechnique) · Jinxing Yu (Baidu Research) · Zhiqiang Xu (Baidu Inc.) · Ping Li (Baidu Research USA)
• Weakly Supervised Deep Functional Maps for Shape Matching
// Abhishek Sharma (Ecole Polytechnique) · Maks Ovsjanikov (Ecole polytechnique)